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Construction progress monitoring is an essential but time-consuming work on all construction sites. This research introduces a method to facilitate the asplanned versus as-built comparison through image based monitoring. A dense point cloud is reconstructed from the images that is compared to an existing 4D building information model (BIM). However, due to the numerous obstructions found on a construction site, only a minority of building elements can be detected directly. In this paper, we discuss how the detection results are significantly refined and enriched by using additional spatial and temporal information gained from the 4D BIM. In this regard, a precedence relationship graph is derived which helps to identify occluded elements and enhance the detection algorithm.

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ABSTRACT:Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.